Abstract: Artificial intelligence systems for system forensics and performance optimization through continuous learning models are disclosed. The described framework includes an orchestrator managing AI agents, which analyze multimodal event data to detect anomalies and identify root causes. The system utilizes a lifelong learning repository to store and access historical data, enabling continuous adaptation and expert-in-the-loop feedback. The method involves receiving multimodal event data, managing AI agents to detect anomalies, and recommending resolutions based on historical data and expert validation.

Abstract: A method that includes obtaining, from one or more stations, embedding vectors that embed features of the stations, obtaining measurement vectors and associated measurement names, generating a text array of the measurement names utilizing a language model, concatenating the text array and the measurement vector at one or more cross-attention modules configured to encode one or more measurement arrays to one or more latent embedding vectors, generating one or more latent embedding vectors associated with the measurement vector and corresponding measurement names via the cross-attention module and a fixed-size station embedding vector, outputting the latent embeddings; generating a query vector; generating key vectors value vectors utilizing a latent embedding vector; decoding the latent vectors utilizing the key vector and value vector; utilizing the cross attention modules and query vectors, decoding the latent embedding vectors; and output a predication.

Abstract: Methods and systems for smoothening the transition of reward systems or datasets for actor-critic reinforcement learning models. A reinforcement model such as an actor-critic model is trained on a first dataset and a first reward system. The weights of the actor model and the critic model are frozen. While these weights are frozen, an affine transformation layer is attached to a final layer of the critic model, and the affine transformation layer is trained with a second dataset and a second reward system in order to adjust a weight of the final layer of the critic model. Then, the weights of the critic model are unfrozen which allows the adjusted weight of the final layer of the critic model to be implemented. The reinforcement learning model is retrained on the second dataset and second reward system, first with just the critic weights unfrozen, and then with both actor and critic weights unfrozen.

Abstract: Methods and systems of optimizing battery charging are disclosed. Battery state sensors are used to determine anode overpotential of a battery multiple times during multiple charge cycles. In a first phase, a reinforcement learning model (e.g., actor-critic model) is trained with rewards given throughout each charge cycle of the battery to optimize training. The reinforcement learning model can determine state-of-health characteristics of the battery over the charge cycles, and in a second phase, the reinforcement learning model is augmented accordingly. During this augmentation, the reinforcement learning model is trained with rewards given on a charge cycle-by-cycle basis, wherein rewards are given after looking at the charging optimization after the conclusion of each charge cycle.

Abstract: A systems and methods for implementing attention-based neural networks, attention modules, regularization techniques, and unique data encoding such as for sequential tabular data and/or manufacturing data is provided. The attention-based neural networks may include a high dropout and unique softmax regularization. The encoding may attend to missing or undefined data as well as numerous data types common to manufacturing data.

Abstract: A systems and methods for implementing attention-based neural networks, attention modules, regularization techniques, and unique data encoding such as for sequential tabular data and/or manufacturing data is provided. The attention-based neural networks may include a high dropout and unique softmax regularization. The encoding may attend to missing or undefined data as well as numerous data types common to manufacturing data.

Abstract: A systems and methods for implementing attention-based neural networks, attention modules, regularization techniques, and unique data encoding such as for sequential tabular data and/or manufacturing data is provided. The attention-based neural networks may include a high dropout and unique softmax regularization. The encoding may attend to missing or undefined data as well as numerous data types common to manufacturing data.

Abstract: A systems and methods for implementing attention-based neural networks, attention modules, regularization techniques, and unique data encoding such as for sequential tabular data and/or manufacturing data is provided. The attention-based neural networks may include a high dropout and unique softmax regularization. The encoding may attend to missing or undefined data as well as numerous data types common to manufacturing data.

Abstract: A method that includes obtaining, from one or more stations, embedding vectors that embed features of the stations, obtaining measurement vectors and associated measurement names, generating a text array of the measurement names utilizing a language model, concatenating the text array and the measurement vector at one or more cross-attention modules configured to encode one or more measurement arrays to one or more latent embedding vectors, generating one or more latent embedding vectors associated with the measurement vector and corresponding measurement names via the cross-attention module and a fixed-size station embedding vector, outputting the latent embeddings; generating a query vector; generating key vectors value vectors utilizing a latent embedding vector; decoding the latent vectors utilizing the key vector and value vector; utilizing the cross attention modules and query vectors, decoding the latent embedding vectors; and output a predication.

Abstract: The invention relates to a method (100) for dimension reduction of a multi-dimensional feature space for training a machine learning model (50) by machine learning, comprising the following steps: providing (101) at least one data pair (30), in which in each case an original data element (31) and a modified data element (32) have a feature difference (?f) in relation to one another, which feature difference is specific to a respective defined task for machine learning, determining (102) at least one task-specific feature space, which is specific to the at least one feature difference (?f), on the basis of a comparison of the respective data pairs (30), performing (103) the dimension reduction on the basis of the determined task-specific feature space.

Abstract: The invention relates to a method for weighting a dataset for training a machine learning model, comprising the following steps: ascertaining (101) a position of each data point (1) of the dataset in a feature space (2) of a further machine learning model, ascertaining (102) a respective proximity of the data points (1) to at least one surrounding data point (1?) in the feature space (2) of the further machine learning model based on the position of the data points (1) ascertained, determining (103) a weighting value for each data point (1) based on the proximity to the at least one surrounding data point (1?) ascertained, using (104) the determined weighting values of the data points (1) when training the machine learning model, wherein the weighting values determined are used for an extent of consideration of the respective data points (1) in the training. The invention further relates to a computer program, a device, and a storage medium for this purpose.

Abstract: Methods and systems for smoothening the transition of reward systems or datasets for actor-critic reinforcement learning models. A reinforcement model such as an actor-critic model is trained on a first dataset and a first reward system. The weights of the actor model and the critic model are frozen. While these weights are frozen, an affine transformation layer is attached to a final layer of the critic model, and the affine transformation layer is trained with a second dataset and a second reward system in order to adjust a weight of the final layer of the critic model. Then, the weights of the critic model are unfrozen which allows the adjusted weight of the final layer of the critic model to be implemented. The reinforcement learning model is retrained on the second dataset and second reward system, first with just the critic weights unfrozen, and then with both actor and critic weights unfrozen.

Abstract: Methods and systems of optimizing battery charging are disclosed. Battery state sensors are used to determine anode overpotential of a battery multiple times during multiple charge cycles. In a first phase, a reinforcement learning model (e.g., actor-critic model) is trained with rewards given throughout each charge cycle of the battery to optimize training. The reinforcement learning model can determine state-of-health characteristics of the battery over the charge cycles, and in a second phase, the reinforcement learning model is augmented accordingly. During this augmentation, the reinforcement learning model is trained with rewards given on a charge cycle-by-cycle basis, wherein rewards are given after looking at the charging optimization after the conclusion of each charge cycle.

Abstract: Systems and methods of optimizing a charging of a vehicle battery are disclosed. Using one or more electronic battery sensors, observable battery state data is determined regarding the charging of the battery. A neural network feature extractor extracts features from preceding vehicle battery state information. A reinforcement learning model, such as an actor-critic model, includes an actor model configured to produce an output associated with a charge command to charge the battery, and a critic model configured to output a predicted reward. The reinforcement learning model is trained based on the vehicle battery state information and the extracted features. This includes updating weights of the actor model to maximize the predicted reward output by the critic model, and updating weights of the feature extractor and weights of the critic model to minimize a difference between the predicted reward and health-based rewards received from charging the battery.

Abstract: The invention provides processes and intermediates useful for preparing integrase inhibiting compounds.